Author: Liam Parker
AI and Creativity: Tools, Taste, and Craft — What the Evidence Says and Practical Steps for Creators
This evidence-based overview examines how AI and creativity are changing creative work, taste, and craft. It summarizes observable signals, reported benefits and limits, documented risks (legal, economic, cultural), how different groups are affected, and practical guidance creators and organizations can use now.
Using AI to Get Hired Ethically: A Practical, ROI-Focused Playbook for Jobseekers and Freelancers
A realistic guide to using AI to get hired ethically — who benefits, business models you can offer, step-by-step execution, costs, timelines, compliance risks (EEOC/FTC/LinkedIn), and the metrics that prove ROI. Includes tool-cost ranges, disclosure best practices, and recovery steps if things go wrong.
Build an AI Course People Finish: A Practical, ROI-Focused Playbook
A business-first guide to designing, pricing, and delivering an AI course that students actually complete. Covers cohort vs self-paced models, step-by-step execution, realistic costs and timelines, compliance (GDPR/COPPA/WCAG), production and platform fees, conversion benchmarks, and the metrics you must track to measure ROI.
AI industry outlook: Signals to Track for 2026 — Verified trends, drivers, and what to monitor
A careful, evidence-led review of the AI industry outlook: verified trends (open models, multimodal progress, enterprise scaling challenges), the forces behind them (compute, data, standards), areas of expert disagreement, practical implications for teams, and a prioritized watchlist of measurable signals and metrics.
AI compute costs: current trends in compute, costs, and efficiency
This evidence-led analysis examines how AI compute costs are shifting today — driven by new accelerator generations, software efficiency (quantization, distillation, sparse models), benchmarking results, and rising energy demands — and separates well-documented signals from open uncertainties relevant to engineering and procurement decisions.
How AI Changes Organizations: Practical Evidence on Work, Structure, and Risk
This evidence-focused article examines how AI changes organizations today: observable signals of adoption, reported benefits and limits, documented concerns and their evidence levels, who gains or loses, and practical advice leaders and workers can use to navigate change responsibly.
AI and education: New norms for classrooms, assessment, and learning support
This evidence-focused article examines how AI is changing education—from classroom practice and tutoring to assessment and policy—by summarizing observable signals, reported benefits, documented risks, uneven effects across groups, and practical steps educators, students, and leaders can take. Sources include OECD, UNESCO, US Department of Education, randomized trials and peer-reviewed reviews.
Retrieval-Centric AI: Why Search Is Back and What That Means for AI Teams
Retrieval-Centric AI—the renewed focus on retrieval-augmented systems and vector search—has reshaped how organizations keep generative models accurate, up to date, and auditable. This evidence-led article separates verified signals from open questions, explains the technical and market drivers, and offers practical steps teams can take now.
Multimodal AI: The New Default Interface — Evidence, Drivers, and Practical Implications
A grounded, evidence-led examination of how multimodal AI is shifting from research curiosity to a practical interface layer. This article separates documented signals (product launches, benchmarks, standards activity) from areas of uncertainty, and translates implications for teams, creators, and users.
AI and Information Quality: How Generative Systems Are Changing What We Trust and Why It Matters
Generative AI is reshaping how people encounter facts, news, and creative work. This evidence-focused guide explains what’s changing in information quality, what people report as benefits, documented risks (with sources), who is most affected, and practical steps readers can take to evaluate and use AI responsibly.